COVID-19
Katie Forthman
Tue May 5 10:32:23 2020
https://kforthman.shinyapps.io/500citiescounties/
#
#remove scientific notation
options(scipen=999)—- Load packages —-
library(stringr)
library(corrplot)## corrplot 0.84 loadedlibrary(shiny)
library(lme4)## Loading required package: Matrixlibrary(lmerTest)##
## Attaching package: 'lmerTest'## The following object is masked from 'package:lme4':
##
## lmer## The following object is masked from 'package:stats':
##
## step—- Load neighborhood data —-
load("Data/county_factors.rda")
load("Data/county_500CitiesData.rda")—- Load and format covid data —-
data.path <- "Data/COVID-19/csse_covid_19_data/csse_covid_19_time_series/"
# Read in the data
US.deaths <- read.csv(
paste0(data.path, "time_series_covid19_deaths_US.csv"),
header = T, stringsAsFactors = F)
US.cases <- read.csv(
paste0(data.path, "time_series_covid19_confirmed_US.csv"),
header = T, stringsAsFactors = F)
# Read in the header seprately.
US.cases.head <- read.csv(
paste0(data.path, "time_series_covid19_confirmed_US.csv"),
header = F, stringsAsFactors = F)[1,]
US.deaths.head <- read.csv(
paste0(data.path, "time_series_covid19_deaths_US.csv"),
header = F, stringsAsFactors = F)[1,]
# Correct the dates in the header to be more useable as
# column names.
proper_date <- function(dates){
dates <- sapply(dates, strsplit, split = "/")
dates <- lapply(dates, str_pad, width = 2, side = "left", pad = "0")
dates <- lapply(dates, paste, collapse = "_")
dates <- unlist(dates)
return(dates)
}
dates.cases <- proper_date(US.cases.head[-c(1:11)])
dates.deaths <- proper_date(US.deaths.head[-c(1:12)])
names(US.cases) <- c(US.cases.head[1,1:11], dates.cases)
names(US.deaths) <- c(US.deaths.head[1,1:12], dates.deaths)
if(sum(US.cases$UID != US.deaths$UID, na.rm = T) > 0){warning("COVID data rows do not match!")}
US.cases$Population <- US.deaths$Population
US.cases <- US.cases[,c(1:11, ncol(US.cases), 12:(ncol(US.cases)-1))]Other stats within the daily reports
data.path <- "Data/COVID-19/csse_covid_19_data/csse_covid_19_daily_reports_us/"
daily_filenames <- list.files(data.path)
daily_filenames <- daily_filenames[daily_filenames != "README.md"]
todays_report_filename <- daily_filenames[length(daily_filenames)]
US.todaysReport <- read.csv(
paste0(data.path, todays_report_filename),
header = T, stringsAsFactors = F)all.states <- c('Alabama', 'Alaska', 'American Samoa', 'Arizona', 'Arkansas', 'California', 'Colorado', 'Connecticut', 'Delaware', 'Diamond Princess', 'District of Columbia', 'Florida', 'Georgia', 'Grand Princess', 'Guam', 'Hawaii', 'Idaho', 'Illinois', 'Indiana', 'Iowa', 'Kansas', 'Kentucky', 'Louisiana', 'Maine', 'Maryland', 'Massachusetts', 'Michigan', 'Minnesota', 'Mississippi', 'Missouri', 'Montana', 'Nebraska', 'Nevada', 'New Hampshire', 'New Jersey', 'New Mexico', 'New York', 'North Carolina', 'North Dakota', 'Northern Mariana Islands', 'Ohio', 'Oklahoma', 'Oregon', 'Pennsylvania', 'Puerto Rico', 'Rhode Island', 'South Carolina', 'South Dakota', 'Tennessee', 'Texas', 'Utah', 'Vermont', 'Virgin Islands', 'Virginia', 'Washington', 'West Virginia', 'Wisconsin', 'Wyoming')
all.states.df <- data.frame(Province_State = all.states)
all.stats <- c("Confirmed", "Deaths", "Recovered", "Active", "Incident_Rate", "People_Tested", "People_Hospitalized", "Mortality_Rate", "Testing_Rate", "Hospitalization_Rate")
compiled.stats <- list()
for(i in 1:length(daily_filenames)){
day <- substring(daily_filenames[i],1,10)
data <- read.csv(
paste0(data.path, daily_filenames[i]),
header = T, stringsAsFactors = F)
compiled.stats[[i]] <- merge(all.states.df, data, all.y = F)
names(compiled.stats)[i] <- day
}Functions for compiling and visualizing stats in the daily reports.
plot.dailyStat <- function(state, stat){
data <- sapply(1:length(daily_filenames), function(x){compiled.stats[[x]][compiled.stats[[x]]$Province_State == state, stat]})
names(data) <- daily_filenames
barplot(data, main = paste0(state, " ", stat), las = 2, cex.axis = 1, cex.names = 0.5)
}
plot.dailyStatRise <- function(state, stat){
data <- sapply(1:length(daily_filenames), function(x){compiled.stats[[x]][compiled.stats[[x]]$Province_State == state, stat]})
names(data) <- daily_filenames
rise.stat <- matrix(ncol = length(data) - 1, nrow = 1)
colnames(rise.stat) <- names(data)[-1]
for(i in 1:ncol(rise.stat) + 1){
rise <- data[i] - data[i-1]
rise.stat[i-1] <- rise
}
barplot(rise.stat, main = paste0(state, " rise in ",stat), las = 2, cex.axis = 1, cex.names = 0.5)
}testing.data.state <- compiled.stats[[length(daily_filenames)]][, c("Province_State", "Testing_Rate")]
testing.data.state <- testing.data.state[!is.na(testing.data.state$Testing_Rate),]
testing.data.state <- testing.data.state[order(testing.data.state$Testing_Rate),]
col.state <- rep("pink", nrow(testing.data.state))
avg.test.rate <- mean(testing.data.state$Testing_Rate, na.rm = T)
col.state[testing.data.state$Testing_Rate < avg.test.rate] <- "grey"
col.state[testing.data.state$Province_State == "Oklahoma"] <- "lightblue"
par(mar = c(5,6,4,2))
barplot(testing.data.state$Testing_Rate, names.arg = testing.data.state$Province_State, horiz = T, main = "Testing Rate by State", las = 2, cex.axis = 1, cex.names = 0.5, col = col.state, border = F, xlab = "Total number of people tested per 100,000 persons.")
abline(v = avg.test.rate, col = "red")
text(x = avg.test.rate + 10, y = 1, labels = "Average Testing Rate", adj = c(0, 0.5), col = "red")
Interactive Plots
Province_State - The name of the State within the USA. Country_Region - The name of the Country (US). Last_Update - The most recent date the file was pushed. Lat - Latitude. Long_ - Longitude. Confirmed - Aggregated confirmed case count for the state. Deaths - Aggregated Death case count for the state. Recovered - Aggregated Recovered case count for the state. Active - Aggregated confirmed cases that have not been resolved (Active = Confirmed - Recovered - Deaths). FIPS - Federal Information Processing Standards code that uniquely identifies counties within the USA. Incident_Rate - confirmed cases per 100,000 persons. People_Tested - Total number of people who have been tested. People_Hospitalized - Total number of people hospitalized. Mortality_Rate - Number recorded deaths * 100/ Number confirmed cases. UID - Unique Identifier for each row entry. ISO3 - Officialy assigned country code identifiers. Testing_Rate - Total number of people tested per 100,000 persons. Hospitalization_Rate - Total number of people hospitalized * 100/ Number of confirmed cases.
Split the dataset into the data and the info for usability.
US.cases.info <- as.matrix(US.cases[,1:12])
US.cases.data <- as.matrix(US.cases[,-c(2:12)])
US.deaths.info <- as.matrix(US.deaths[,1:12])
US.deaths.data <- as.matrix(US.deaths[,-c(2:12)])
rownames(US.cases.info) <- US.cases.info[,1]
US.cases.info <- US.cases.info[,-1]
rownames(US.cases.data) <- US.cases.data[,1]
US.cases.data <- US.cases.data[,-1]
rownames(US.deaths.info) <- US.deaths.info[,1]
US.deaths.info <- US.deaths.info[,-1]
rownames(US.deaths.data) <- US.deaths.data[,1]
US.deaths.data <- US.deaths.data[,-1]
ndays.cases <- ncol(US.cases.data)
ndays.deaths <- ncol(US.deaths.data)
nobs <- nrow(US.cases.data)—- The Curve —-
state.curve <- function(state, stat = c("cases", "deaths"), logScale = T){
if(stat == "cases"){
data <- US.cases.data[which(US.cases$Province_State == state),]
}else if(stat == "deaths"){
data <- US.deaths.data[which(US.deaths$Province_State == state),]
}
data.sum <- colSums(data)
day.first.case <- min(which(data.sum > 0))
n.days <- length(data.sum)
if(logScale == T){
barplot(data.sum[day.first.case:n.days],
main = paste0("Total COVID-19 ", stat," by date in ", state, ", log scale"),
log = "y", las = 2, cex.axis = 1, cex.names = 0.5)
}else{
barplot(data.sum[day.first.case:n.days],
main = paste0("Total COVID-19 ", stat," by date in ", state),
las = 2, cex.axis = 1, cex.names = 0.5)
}
}
state.rise <- function(state, stat = c("cases", "deaths")){
if(stat == "cases"){
data.thisState <- US.cases.data[which(US.cases$Province_State == state),]
}else if(stat == "deaths"){
data.thisState <- US.deaths.data[which(US.deaths$Province_State == state),]
}
data.sum <- colSums(data.thisState)
n.days <- ncol(data.thisState)
rise.cases <- matrix(ncol = n.days - 1, nrow = 1)
colnames(rise.cases) <- colnames(data.thisState)[-1]
for(i in 1:ncol(rise.cases) + 1){
rise <- data.sum[i] - data.sum[i-1]
rise.cases[i-1] <- rise
}
day.first.case <- min(which(rise.cases > 0))
n.days <- length(rise.cases)
barplot(rise.cases[,day.first.case:n.days], main = paste0("Rise in COVID-19 ", stat, " by Date in ", state), las = 2, cex.axis = 1, cex.names = 0.5)
}county.curve <- function(county, stat = c("cases", "deaths")){
if(stat == "cases"){
data <- US.cases.data[which(US.cases$Admin2 == county),]
}else if(stat == "deaths"){
data <- US.deaths.data[which(US.deaths$Admin2 == county),]
}
day.first.case <- min(which(data > 0))
n.days <- length(data)
barplot(data[day.first.case:n.days], main = paste0("Total COVID-19 ", stat," by date in ", county), log = "y", las = 2, cex.axis = 1, cex.names = 0.5)
}
county.curve("Tulsa", "cases")
county.curve("Tulsa", "deaths")
—- Calculate some useful stats to compare with neighborhood data —-
US.stats <- data.frame(UID = US.cases$UID)cases.total <- colSums(US.cases.data)
day.first.case <- min(which(cases.total > 100))
n.days <- length(cases.total)
par(mar = c(5,5,4,2))
barplot(cases.total[day.first.case:n.days], main = "Total COVID-19 cases by Date in US", las = 2, cex.axis = 1, cex.names = 0.5)
barplot(cases.total[day.first.case:n.days], main = "Total COVID-19 cases by Date in US, log scale", las = 2, cex.axis = 1, cex.names = 0.5, log = "y")
deaths.total <- colSums(US.deaths.data)
day.first.case <- min(which(deaths.total > 0))
n.days <- length(deaths.total)
barplot(deaths.total[day.first.case:n.days], main = "Total COVID-19 deaths by Date in US", las = 2, cex.axis = 1, cex.names = 0.5)
barplot(deaths.total[day.first.case:n.days], main = "Total COVID-19 deaths by Date in US, log scale", las = 2, cex.axis = 1, cex.names = 0.5, log = "y")
Average rise in cases per day
avg.rise.cases
rise.cases <- matrix(ncol = ndays.cases - 1, nrow = nobs)
colnames(rise.cases) <- colnames(US.cases.data)[-1]
for(i in 1:ncol(rise.cases) + 1){
rise <- US.cases.data[,i] - US.cases.data[,i-1]
rise.cases[,i-1] <- rise
}
US.stats$avg.rise.cases <- apply(rise.cases, 1, mean)
rise.cases.total <- colSums(rise.cases)
day.first.case <- min(which(rise.cases.total > 0))
n.days <- length(rise.cases.total)
barplot(rise.cases.total[day.first.case:n.days], main = "Rise in Cases of COVID-19 by Date in US", las = 2, cex.axis = 1, cex.names = 0.5)
Average rise in deaths per day
avg.rise.deaths
rise.deaths <- matrix(ncol = ndays.deaths - 1, nrow = nobs)
colnames(rise.deaths) <- colnames(US.deaths.data)[-1]
for(i in 1:ncol(rise.deaths) + 1){
rise <- US.deaths.data[,i] - US.deaths.data[,i-1]
rise.deaths[,i-1] <- rise
}
US.stats$avg.rise.deaths <- apply(rise.deaths, 1, mean)
rise.deaths.total <- colSums(rise.deaths)
day.first.case <- min(which(rise.deaths.total > 0))
n.days <- length(rise.deaths.total)
barplot(rise.deaths.total[day.first.case:n.days], main = "Rise in Deaths of COVID-19 by Date in US", las = 2, cex.axis = 1, cex.names = 0.5)
Total cases
total.cases
US.stats$total.cases <- US.cases.data[,ndays.cases]Total cases per capita
US.stats$total.cases.percap <- US.stats$total.cases / US.cases$Population
US.stats$total.cases.percap[US.cases$Population == 0] <- NA
hist(US.stats$total.cases.percap)
Total deaths
total.deaths
US.stats$total.deaths <- US.deaths.data[,ndays.deaths]Total deaths per capita
total.deaths.percap
US.stats$total.deaths.percap <- US.stats$total.deaths / US.deaths$Population
US.stats$total.deaths.percap[US.deaths$Population == 0] <- NA
max(US.stats$total.deaths.percap,na.rm = T)## [1] 0.003283872Total deaths per case
total.deaths.percase Error in Johns Hopkins data has rows with total.deaths > total.cases.
# pos.case.ind <- US.stats$total.cases > 0
# US.stats$total.deaths.percase[pos.case.ind] <- US.stats$total.deaths[pos.case.ind] / US.stats$total.cases[pos.case.ind]
# US.stats$total.deaths.percase[!pos.case.ind] <- 0
US.stats$total.deaths.percase <- US.stats$total.deaths / US.stats$total.cases
US.stats$total.deaths.percase[US.stats$total.cases == 0] <- NA
US.stats[which(US.stats$total.deaths > US.stats$total.cases),]## UID avg.rise.cases avg.rise.deaths total.cases
## 3155 84080008 0.0000000 0.01941748 0
## 3204 84090004 0.0000000 0.33009709 0
## 3206 84090006 0.0000000 0.01941748 0
## 3220 84090022 0.6116505 0.70873786 63
## 3222 84090024 0.0000000 0.74757282 0
## 3229 84090031 0.0000000 0.10679612 0
## 3230 84090032 0.0000000 0.02912621 0
## 3231 84090033 0.1456311 0.25242718 15
## 3233 84090035 0.0000000 0.05825243 0
## 3236 84090038 0.0000000 0.05825243 0
## 3252 84090056 0.0000000 0.05825243 0
## total.cases.percap total.deaths total.deaths.percap
## 3155 NA 2 NA
## 3204 NA 34 NA
## 3206 NA 2 NA
## 3220 NA 73 NA
## 3222 NA 77 NA
## 3229 NA 11 NA
## 3230 NA 3 NA
## 3231 NA 26 NA
## 3233 NA 6 NA
## 3236 NA 6 NA
## 3252 NA 6 NA
## total.deaths.percase
## 3155 NA
## 3204 NA
## 3206 NA
## 3220 1.158730
## 3222 NA
## 3229 NA
## 3230 NA
## 3231 1.733333
## 3233 NA
## 3236 NA
## 3252 NA—- Merge COVID data with Neighborhood data —-
US.stats$ID <- str_pad(US.stats$UID, 8, "left", pad = "0")
US.stats$ID <- substr(US.stats$ID, 4, 8)
data.merge <- merge(US.stats, county_factors, by = "ID")—- Plot the relationships —-
data.cor <- cor(data.merge[,-c(1:2)], use = "complete.obs", method = "spearman")
corrplot.mixed(data.cor, upper = 'ellipse', lower = 'number', tl.pos = 'lt', tl.cex = 1, lower.col = "black", number.cex = 0.5)
data.merge2 <- merge(data.merge, county_500CitiesData, by = "ID", all.x = F)—- Plot the relationships —-
data.cor2 <- cor(data.merge2[,-c(1:2)], use = "complete.obs", method = "spearman")
corrplot.mixed(data.cor2, upper = 'ellipse', lower = 'number', tl.pos = 'lt', tl.cex = 1, lower.col = "black", number.cex = 0.5)
corrplot.mixed(data.cor2[1:7,8:42], upper = 'ellipse', lower = 'number', tl.pos = 'lt', tl.cex = 1, lower.col = "black", number.cex = 0.5)
—-Linear Mixed Effects Model —-
US.todaysReport.states <- US.todaysReport[!is.na(US.todaysReport$FIPS) & nchar(US.todaysReport$FIPS)<=2,]
US.todaysReport.states$FIPS <- str_pad(US.todaysReport.states$FIPS, 2, "left", pad = "0")
data.merge2$stateID <- substr(data.merge2$ID,1,2)
data.merge3 <- merge(data.merge2, US.todaysReport.states, by.x = "stateID", by.y = "FIPS")
county.Demo_and_Covid <- data.merge3
save(county.Demo_and_Covid, file = "county.Demo_and_Covid.Rda")
this.lme <- lmer("total.cases.percap ~ Affluence + Singletons.in.Tract + Seniors.in.Tract + African.Americans.in.Tract + Noncitizens.in.Tract + High.BP + Binge.Drinking + Cancer + Asthma + Heart.Disease + COPD + Smoking + Diabetes + No.Physical.Activity + Obesity + Poor.Sleeping.Habits + Poor.Mental.Health + Testing_Rate + Hospitalization_Rate + (1 | stateID)", data = data.merge3)## Warning: Some predictor variables are on very different scales: consider
## rescaling
## Warning: Some predictor variables are on very different scales: consider
## rescalingprint(summary(this.lme), correlation=TRUE)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula:
## "total.cases.percap ~ Affluence + Singletons.in.Tract + Seniors.in.Tract + African.Americans.in.Tract + Noncitizens.in.Tract + High.BP + Binge.Drinking + Cancer + Asthma + Heart.Disease + COPD + Smoking + Diabetes + No.Physical.Activity + Obesity + Poor.Sleeping.Habits + Poor.Mental.Health + Testing_Rate + Hospitalization_Rate + (1 | stateID)"
## Data: data.merge3
##
## REML criterion at convergence: -1138.4
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.7683 -0.3341 -0.0919 0.1782 5.7641
##
## Random effects:
## Groups Name Variance Std.Dev.
## stateID (Intercept) 0.0000007021 0.0008379
## Residual 0.0000126053 0.0035504
## Number of obs: 169, groups: stateID, 32
##
## Fixed effects:
## Estimate Std. Error df
## (Intercept) -0.0078746855 0.0090298040 62.3138355044
## Affluence 0.0043698425 0.0010519500 91.5326788737
## Singletons.in.Tract 0.0017059986 0.0009150532 131.4593204704
## Seniors.in.Tract 0.0009375375 0.0011843016 140.9984983418
## African.Americans.in.Tract 0.0001396996 0.0009954077 142.7064406955
## Noncitizens.in.Tract 0.0008178698 0.0007358216 114.7156862767
## High.BP 0.0002412119 0.0001827021 86.6878878144
## Binge.Drinking 0.0001272692 0.0001459184 34.3261620538
## Cancer -0.0007972244 0.0010497815 82.9691603894
## Asthma 0.0004853341 0.0005090788 31.1164766873
## Heart.Disease 0.0005965888 0.0012220823 60.9601033416
## COPD 0.0000268897 0.0010322652 63.9445400109
## Smoking -0.0001469246 0.0002178419 67.3938169539
## Diabetes -0.0004910568 0.0005179398 65.9315500917
## No.Physical.Activity 0.0000165102 0.0001944344 73.0009159092
## Obesity 0.0001928679 0.0001675970 79.4067946110
## Poor.Sleeping.Habits -0.0000004496 0.0001615245 116.4925316048
## Poor.Mental.Health -0.0000325802 0.0003844988 26.7523010992
## Testing_Rate 0.0000004884 0.0000003230 27.0157178141
## Hospitalization_Rate -0.0001180858 0.0000815336 25.4805497692
## t value Pr(>|t|)
## (Intercept) -0.872 0.3865
## Affluence 4.154 0.0000733 ***
## Singletons.in.Tract 1.864 0.0645 .
## Seniors.in.Tract 0.792 0.4299
## African.Americans.in.Tract 0.140 0.8886
## Noncitizens.in.Tract 1.112 0.2687
## High.BP 1.320 0.1902
## Binge.Drinking 0.872 0.3892
## Cancer -0.759 0.4498
## Asthma 0.953 0.3478
## Heart.Disease 0.488 0.6272
## COPD 0.026 0.9793
## Smoking -0.674 0.5023
## Diabetes -0.948 0.3465
## No.Physical.Activity 0.085 0.9326
## Obesity 1.151 0.2533
## Poor.Sleeping.Habits -0.003 0.9978
## Poor.Mental.Health -0.085 0.9331
## Testing_Rate 1.512 0.1421
## Hospitalization_Rate -1.448 0.1597
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1##
## Correlation of fixed effects could have been required in summary()##
## Correlation of Fixed Effects:
## (Intr) Afflnc Sng..T Snr..T A.A..T Nnc..T Hgh.BP Bng.Dr Cancer
## Affluence 0.151
## Sngltns.n.T -0.007 0.054
## Snrs.n.Trct 0.586 0.380 0.173
## Afrcn.Am..T 0.187 0.162 -0.432 0.168
## Nnctzns.n.T -0.013 0.097 0.049 0.059 -0.073
## High.BP 0.023 0.248 0.096 0.137 -0.107 0.396
## Bing.Drnkng -0.246 -0.181 -0.303 -0.174 0.114 0.052 0.147
## Cancer -0.597 -0.208 0.181 -0.341 -0.076 -0.155 -0.403 -0.134
## Asthma -0.351 -0.200 -0.200 -0.151 0.086 0.092 0.165 -0.014 0.038
## Heart.Dises -0.146 0.077 -0.285 -0.149 0.238 -0.097 -0.033 0.063 -0.460
## COPD 0.551 0.036 0.133 0.269 0.004 0.289 0.220 0.125 -0.267
## Smoking -0.191 0.101 -0.180 -0.132 -0.090 -0.008 -0.106 -0.297 0.086
## Diabetes 0.055 -0.316 -0.165 -0.241 -0.278 -0.329 -0.526 0.037 0.220
## N.Physcl.Ac -0.175 -0.061 0.104 -0.022 -0.025 -0.228 -0.124 0.085 0.495
## Obesity 0.026 0.442 0.397 0.311 0.163 0.218 -0.063 -0.219 0.105
## Pr.Slpng.Hb -0.501 -0.410 0.181 -0.391 -0.405 0.008 -0.187 0.062 0.176
## Pr.Mntl.Hlt -0.321 0.257 -0.056 -0.066 0.111 -0.205 -0.102 0.036 0.312
## Testing_Rat 0.191 -0.113 -0.090 -0.019 0.042 -0.101 -0.004 0.033 -0.196
## Hsptlztn_Rt -0.115 -0.235 -0.172 -0.259 -0.074 -0.130 -0.147 -0.152 0.047
## Asthma Hrt.Ds COPD Smokng Diabts N.Ph.A Obesty Pr.S.H Pr.M.H
## Affluence
## Sngltns.n.T
## Snrs.n.Trct
## Afrcn.Am..T
## Nnctzns.n.T
## High.BP
## Bing.Drnkng
## Cancer
## Asthma
## Heart.Dises 0.273
## COPD -0.361 -0.565
## Smoking 0.072 0.220 -0.529
## Diabetes -0.124 -0.233 -0.165 0.294
## N.Physcl.Ac 0.010 -0.397 0.004 -0.349 -0.089
## Obesity -0.279 -0.109 0.189 -0.224 -0.406 -0.051
## Pr.Slpng.Hb 0.084 0.244 -0.218 0.043 -0.017 -0.098 -0.172
## Pr.Mntl.Hlt -0.221 0.096 -0.461 0.084 0.038 0.077 0.091 -0.192
## Testing_Rat -0.363 -0.021 0.177 0.177 0.160 -0.330 0.053 -0.142 -0.115
## Hsptlztn_Rt 0.009 0.077 -0.104 0.153 0.152 -0.067 -0.146 0.014 -0.017
## Tstn_R
## Affluence
## Sngltns.n.T
## Snrs.n.Trct
## Afrcn.Am..T
## Nnctzns.n.T
## High.BP
## Bing.Drnkng
## Cancer
## Asthma
## Heart.Dises
## COPD
## Smoking
## Diabetes
## N.Physcl.Ac
## Obesity
## Pr.Slpng.Hb
## Pr.Mntl.Hlt
## Testing_Rat
## Hsptlztn_Rt 0.312
## fit warnings:
## Some predictor variables are on very different scales: consider rescalingthis.lme.sum <- summary(this.lme)